1. Technical Field
The present invention generally relates to a predictive maintenance system and, more particularly, to a predictive maintenance system using a local area network (LAN) to control, to acquire, and to analyze data from a plurality of remote data acquisition nodes.
2. Description of Related Art
Various systems are known for analyzing vibration signatures of machines. One such prior art system uses a portable data collector having sensors which are connectable by a technician to a machine at one or more points to collect vibration data. The vibration signatures thus collected are downloaded to a personal computer, for example, which includes software for analyzing the vibration data. The software uses artificial intelligence, for example, to diagnose the risk of machine breakdown by first comparing the vibration signatures to earlier baseline signatures and then comparing any changes to a database of machine health-related characteristics and a knowledge base containing fault related scenarios. Since tiny changes in vibration data can indicate a machine fault, the software can predict the risk of breakdown. Such predictions can be used to shut down and/or effect repairs to machines before a catastrophic breakdown occurs, possibly resulting in reduced plant operations or, in some instances, plant shutdown.
Also known are predictive maintenance systems which do not require a technician to walk through a plant or facility to collect data. Such systems use permanently mounted sensors which are connected to a system controller for collecting vibration data. However, while such so-called "on-line" data collection systems are available, they generally function to collect data only and cannot perform automatic real-time expert analysis as part of the system's functionality. In addition, such systems often have complicated architectures. Still further, these systems generally have a text and menu driven interface, making it difficult for a user to configure and use the system.